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app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load the pre-trained Fashion MNIST model
+model_path = "kia_fashion_mnist_keras_model.h5"
+model = tf.keras.models.load_model(model_path)
+
+# Labels for Fashion MNIST
+labels = [
+    'T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
+    'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'
+]
+
+def predict_fashion(image):
+    # Convert image to grayscale if not already and resize
+    image = Image.fromarray(image).convert('L').resize((28, 28))
+    # Normalize the image
+    image = np.array(image) / 255.0
+    # Reshape for model input
+    image = image.reshape(1, 28, 28, 1)
+
+    # Make a prediction
+    predictions = model.predict(image)
+    prediction = np.argmax(predictions, axis=1)[0]
+    confidence = np.max(predictions)
+
+    # Prepare the output
+    result = f"Predicted category: {labels[prediction]} with confidence: {confidence:.2f}"
+    return result
+
+# Create Gradio interface
+input_image = gr.Image(image_mode='L')
+output_label = gr.Label()
+interface = gr.Interface(fn=predict_fashion,
+                         inputs=input_image,
+                         outputs=output_label,
+                         examples=["images/0.png", "images/1.png", "images/2.png", "images/3.png"],
+                         title="Fashion MNIST Classifier",
+                         description="Drag and drop an image or select an example below to predict the fashion category.")
+
+# Launch the interface
+interface.launch()

kia_fashion_mnist_keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a74c184830e9363668121b5767ef4627dde185d6e5e474d025fea66b286ddd05
+size 20068320